Minimising Backbreak at the Dewan Cement Limestone Quarry Using an Artificial Neural Network

Muhammad, Khan; Shah, A. N.

doi:10.1515/amsc-2017-0055

Cited by 2 publications

(1 citation statement)

References 31 publications

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“…Gates et al, (2005) stated that the leading cause of the generation of back break is shorter delay interval and when the number of rows of holes increases, then back break also increases. Monjezi et al, (2012) stated that hole depth, stemming length, spacing, and burden are the most important parameters that in uence the propagation of back break (Muhammad and Shah 2017). Jimeno, Jimino and Carcedo, (1995) nd out that the generation of back break is related to excessive burden and low stiffness ratio.…”

Section: Improper Fragmentationmentioning

confidence: 99%

Prediction and Assessment of Back Break by Multivariate Regression Analysis, and Random Forest algorithm in hot strata/fiery seam of open-pit coal mine

Sharma

Choudhary

Agrawal³

2021

Preprint

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Coal seam in mines sometimes catches fire due to its property of spontaneous heating. Drilling and blasting techniques are the most economical operation for the removal of overburden and extraction of coal in an open-pit coal mine. Blasting in a fiery seam is one the most dangerous and risky operations and various environmental and technical problems are associated with it. While blasting in a fiery coal seam, it is essential to use the minimum amount of explosive in a hole and the blast should be taken quickly. As the amount of explosive to be used must be comparatively lesser; therefore, it becomes important to perform the blasting in a manner to maximize the utilization of explosive energy. Improper utilization of energy causes back break, which is one of the technical concerns for management due to the in-stability of high wall. This study is a unique attempt to predict back break, especially in fiery coal seam through the experimental blasts and their analysis by Multivariate Regression Analysis (MVRA), and Random Forest algorithm (RFA). Total 26 blasting were conducted at open pit mine. To obtain the most optimum blast design parameter, blast design parameters were varied. The observed back break during trial blasts varies from 1.4 to 10 m. The Sensitivity analysis performed with the collected data and it has been found that stiffness ratio and stemming length are the most influential parameters on generation of back break. The MVRA and RFA analysis have been adopted for accurate prediction of back break. The results show that the RFA analysis predicts the backbreak and achieved the RMSE of ± 0.59 m while in MVRA, RMSE achieved was ± 0.99 m, making RFA more suitable method to be used for backbreak prediction in fiery seam blasting.

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Section: Improper Fragmentationmentioning

confidence: 99%

Prediction and Assessment of Back Break by Multivariate Regression Analysis, and Random Forest algorithm in hot strata/fiery seam of open-pit coal mine

Sharma

Choudhary

Agrawal³

2021

Preprint

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Development of a universal blast-induced ground vibration prediction model for Jharia coalfields

Garai¹,

Mishra²,

Kumar³

et al. 2018

MMC_C

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Mineral commodities are the backbone of every nation as they contribute to the Gross domestic product (GDP), industries (as raw materials), and foreign exchange. Coal production in India is 724.71 MT (Million Tonnes) in year 2015. The production target has been increased by the government of India to 1.5 billion tonnes by 2022. Opencast mining in 2015 contributed to 639.234 MT (88%) of total coal production in India. This increased demand of coal shall compel the mine owner to operate under greater stress to increase the production rate. So, there will be situations arising that the mine owners may have to extract coal near human habitats. Eight of the major mines were selected and necessary care was taken that all the geological areas occurring in the Jharia coalfield region was covered. Various blast design parameters such burden, hole depth, spacing, charge per delay(CPD), and scaled distance were selected for the study of ground vibration. Data mining methods such as random forest and artificial neural networks(ANN) has been used for the prediction of peak particle velocity(PPV). The model formed by the two methods was compared and validated by selecting another two mines. The results obtained by random forest was superior to ANN. Also, a line at 95% confidence interval for the predicted PPV values is drawn to ensure greater safety. With 95% confidence and universal blast induced ground vibration prediction model for jharia coalfield has been developed. This model will help the blasting engineers operating in jharia coalfields in precise prediction and better control over blast induced ground vibration. The concept of this study can be used for generation of blast induced ground vibration prediction models for different coalfields also.

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Minimising Backbreak at the Dewan Cement Limestone Quarry Using an Artificial Neural Network

Cited by 2 publications

References 31 publications

Prediction and Assessment of Back Break by Multivariate Regression Analysis, and Random Forest algorithm in hot strata/fiery seam of open-pit coal mine

Prediction and Assessment of Back Break by Multivariate Regression Analysis, and Random Forest algorithm in hot strata/fiery seam of open-pit coal mine

Development of a universal blast-induced ground vibration prediction model for Jharia coalfields

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